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Distributed Sequential Shrinkage Estimation
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DOI：10.1007/s11424-025-4280-7.png)
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Abstract
En 中文
In econometric analysis, researchers often encounter vast datasets that greatly reduce model efficiency. In this paper, the authors develop a sequential shrinkage estimation method for use in distributed settings. Within this framework, one dataset is split into several blocks, and each block is regarded as a node. The sequential shrinkage estimation method is implemented on the data in each block until the stopping criteria are satisfied. These sequential procedures are then integrated to produce the final results using a weighted average, which provides approximate regression result estimates for the entire dataset. The proposed method can significantly reduce the required number of samples and perform parameter estimation and variable selection while satisfying the preset accuracy requirements. In addition, the statistical properties of the parameter estimates in the proposed approach are analyzed for use in a linear regression model. Finally, numerical studies on simulated and real datasets show that the proposed method performs well.
Keywords:
Distributed computation
econometric models
sequential sampling
shrinkage estimation
variable selection
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2.8
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212
Citations:
2.1K
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